Why Google Seems To Favor Big Brands & Low-Quality Content via @sejournal, @martinibuster

There are many people who are convinced that Google shows a preference for big brands and ranking low quality content, something that many feel has become progressively worse. This may not be a matter of perception, something is going on, nearly everyone has an anecdote of poor quality search results. The possible reasons for it are actually quite surprising.

Google Has Shown Favoritism In The Past

This isn’t the first time that Google’s search engine results pages (SERPs) have shown a bias that favored big brand websites. During the early years of Google’s algorithm it was obvious that sites with a lot of PageRank ranked for virtually anything they wanted.

For example, I remember a web design company that built a lot of websites, creating a network of backlinks, raising their PageRank to a remarkable level normally seen only in big corporate sites like IBM. As a consequence they ranked for the two-word keyword phrase, Web Design and virtually every other variant like Web Design + [any state in the USA].

Everyone knew that websites with a PageRank of 10, the highest level shown on Google’s toolbar, practically had a free pass in the SERPs, resulting in big brand sites outranking more relevant webpages. It didn’t go unnoticed when Google eventually adjusted their algorithm to fix this issue.

The point of this anecdote is to point out an instance of where Google’s algorithm unintentionally created a bias that favored big brands.

Here are are other  algorithm biases that publishers exploited:

  • Top 10 posts
  • Longtail “how-to” articles
  • Misspellings
  • Free Widgets in footer that contained links (always free to universities!)

Big Brands And Low Quality Content

There are two things that have been a constant for all of Google’s history:

  • Low quality content
  • Big brands crowding out small independent publishers

Anyone that’s ever searched for a recipe knows that the more general the recipe the lower the quality of recipe that gets ranked. Search for something like cream of chicken soup and the main ingredient for nearly every recipe is two cans of chicken soup.

A search for Authentic Mexican Tacos results in recipes with these ingredients:

  • Soy sauce
  • Ground beef
  • “Cooked chicken”
  • Taco shells (from the store!)
  • Beer

Not all recipe SERPs are bad. But some of the more general recipes Google ranks are so simple and basic that a hobo can cook them on a hotplate.

Robin Donovan (Instagram), a cookbook author and online recipe blogger observed:

“I think the problem with google search rankings for recipes these days (post HCU) are much bigger than them being too simple.

The biggest problem is that you get a bunch of Reddit threads or sites with untested user-generated recipes, or scraper sites that are stealing recipes from hardworking bloggers.

In other words, content that is anything but “helpful” if what you want is a tested and well written recipe that you can use to make something delicious.”

Explanations For Why Google’s SERPs Are Broken

It’s hard not to get away from the perception that Google’s rankings for a variety of topics always seem to default to big brand websites and low quality webpages.

Small sites grow to become big brands that dominate the SERPs, it happens. But that’s the thing, even when a small site gets big, it’s now another big brand dominating the SERPs.

Typical explanations for poor SERPs:

  • It’s a conspiracy to increase ad clicks
  • Content itself these days are low quality across the board
  • Google doesn’t have anything else to rank
  • It’s the fault of SEOs
  • Affiliates
  • Poor SERPs is Google’s scheme to drive more ad clicks
  • Google promotes big brands because [insert your conspiracy]

So what’s going on?

People Love Big Brands & Garbage Content

The recent Google anti-trust lawsuit exposed the importance of the Navboost algorithm signals as a major ranking factor. Navboost is an algorithm that interprets user engagement signals to understand what topics a webpage is relevant for, among other things.

The idea of using engagement signals as an indicator of what users expect to see makes sense. After all, Google is user-centric and who better to decide what’s best for users than the users themselves, right?

Well, consider that arguably the the biggest and most important song of 1991, Smells Like Teen Spirt by Nirvana, didn’t make the Billboard top 100 for that year. Michael Bolton and Rod Stewart made the list twice, with Rod Stewart top ranked for a song called “The Motown Song” (anyone remember that one?)

Nirvana didn’t make the charts until the next year…

My opinion, given that we know that user interactions are a strong ranking signal, is that Google’s search rankings follow a similar pattern related to users’ biases.

People tend to choose what they know. It’s called a Familiarity Bias.

Consumers have a habit of choosing things that are familiar over those that are unfamiliar. This preference shows up in product choices that prefer brands, for example.

Behavioral scientist, Jason Hreha, defines Familiarity Bias like this:

“The familiarity bias is a phenomenon in which people tend to prefer familiar options over unfamiliar ones, even when the unfamiliar options may be better. This bias is often explained in terms of cognitive ease, which is the feeling of fluency or ease that people experience when they are processing familiar information. When people encounter familiar options, they are more likely to experience cognitive ease, which can make those options seem more appealing.”

Except for certain queries (like those related to health), I don’t think Google makes an editorial decision to certain kinds of websites, like brands.

Google uses many signals for ranking. But Google is strongly user focused.

I believe it’s possible that strong user preferences can carry a more substantial weight than Reviews System signals. How else to explain why Google seemingly has a bias for big brand websites with fake reviews rank better than honest independent review sites?

It’s not like Google’s algorithms haven’t created poor search results in the past.

  • Google’s Panda algorithm was designed to get rid of a bias for cookie cutter content.
  • The Reviews System is a patch to fix Google’s bias for content that’s about reviews but aren’t necessarily reviews.

If Google has systems for catching low quality sites that their core algorithm would otherwise rank, why do big brands and poor quality content still rank?

I believe the answer is that is what users prefer to see those sites, as indicated by user interaction signals.

The big question to ask is whether Google will continue to rank what users biases and inexperience trigger user satisfaction signals.  Or will Google continue serving the sugar-frosted bon-bons that users crave?

Should Google make the choice to rank quality content at the risk that users find it too hard to understand?

Or should publishers give up and focus on creating for the lowest common denominator like the biggest popstars do?

Google Launches “Help Me Write” AI Assistant For Chrome Browser via @sejournal, @MattGSouthern

Google is releasing a new AI feature in the Chrome web browser that can help you compose written content.

The “Help Me Write” tool, announced last month and launching this week, assists with crafting everything from online reviews to inquiries and classified ads.

Everyday Writing Made Easier

Utilizing Google’s Gemini model, Help Me Write generates text based on the context of the website you’re browsing and the text field you’re writing in.

For example, when selling an item online, Help Me Write may take a brief product description and expand it into a polished, detailed post.

Google states in an announcement:

“The tool will understand the context of the webpage you’re on to suggest relevant content.

For example, if you’re writing a review for a pair of running shoes, Chrome will pull out key features from the product page that support your recommendation so it’s more valuable to potential shoppers.”

Examples Of Help Me Write In Action

To demonstrate how Help Me Write works, Google provided the following examples.

Example One

Google Launches “Help Me Write” AI Assistant For Chrome Browser

When given the prompt “moving to a smaller place selling airfryer for 50 bucks,” the tool generated a post reading in part: “I’m moving to a smaller place and won’t have any room for my air fryer. It’s in good condition and works great. I’m selling it for $50.”

Example Two

Google Launches “Help Me Write” AI Assistant For Chrome Browser

Given the prompt “plane lands at 9 – ask to check in early,” it composed a hotel inquiry: “My flight is scheduled to arrive at 9 am, and I would like to check in as soon as possible. Is there any way I can check in early?”

Example Three

Google Launches “Help Me Write” AI Assistant For Chrome Browser

For the prompt “write a request to return a defective bike helmet that has a line crack despite not stated as covered in the product warranty,” Help Me Write suggested: “I would like to return a bike helmet that I recently purchased. The helmet developed a crack along the line where the helmet is joined together. This crack was not caused by an impact or other damage…”

How to Enable “Help Me Write”

To enable Help Me Write, Chrome users can navigate to the “Experimental AI” section of their browser settings.

This feature, integrated into the latest Chrome M122 update, is now available for English-language users in the United States on Mac and Windows PCs.

You can turn the feature off and on at any time.

Google Analytics Overhauls Ad Reporting In One Central Hub via @sejournal, @MattGSouthern

Google is rolling out an update to simplify reporting in Google Analytics.

The changes, which start rolling out today, consolidate all advertising and publisher reporting into one centralized “Advertising” section.

For digital marketers, SEOs, and publishers, this update helps you monitor and analyze organic website analytics and paid advertising campaigns within the same property.

“The Advertising section will become the hub to monitor and analyze your campaigns whether you’re a publisher or an advertiser,” Google stated, emphasizing this section’s central role from now on.

Enhanced Insights for User Engagement and Campaign Performance

The new structure is designed to give a comprehensive overview of user interactions and campaign data. The Reports section can now provide an in-depth analysis of how users engage with websites and apps,

Other features like the Explore section, Custom Reports, and the Data API are designed to provide both behavioral insights and anonymized, aggregated insights from advertising campaigns.

This enriches the data available for informed decision-making.

Access & Availability

Starting today, all Google Ads, Google Marketing Platform, and publisher reports will now live under the new unified Advertising section.

This centralizes campaign insights that were previously spread across multiple sections.

Accessing the Advertising section will require linking an account like Google Ads, AdSense, or Google Ad Manager. This links insights between ads accounts and Analytics for consolidated reporting.

For those without linked accounts, Google will prompt users to connect to an ads or publisher account, ensuring uninterrupted access to data and reporting features.

Takeaways

The Reports section in Google Analytics will focus only on behavioral analytics like traffic sources, conversions, and user engagement. Advertising and publisher data will all funnel through the new Advertising section.

With this streamlined approach, Google is creating tailored experiences for marketers and publishers.

Those running ad campaigns can monitor them within the Advertising section, while on-site metrics for publishers will remain separate in Reports and other sections.

Features like Custom Reports and the Analytics API will continue providing behavioral and advertising data for full flexibility.


Featured Image: ulkerdesign/Shutterstock

Google Ads Performance Max Image Generation Now Available To All via @sejournal, @MattGSouthern

Google is announcing the launch of its AI-powered tools for generating and editing images in Performance Max campaigns.

These features, initially previewed in November, are now accessible in English worldwide, with more languages to follow.

Additionally, Google is enhancing its Ad Strength indicator to give advertisers more information about their ad assets, including amount and quality.

Here are all the details about the Google Ads updates announced today.

Gemini + Imagen 2 + Performance Max

Performance Max will soon offer new capabilities enabled by Google’s Gemini and Imagen 2 AI models.

Text & Image Generation Gets Smarter

With the incorporation of Gemini models, Performance Max will offer advanced features like long headline generation and upcoming sitelink generation.

These features leverage the reasoning capabilities of Gemini to craft text assets.

Enhancing Images With AI Precision

Google is updating its image generation technology with a new model called Imagen 2.

This model allows advertisers to create marketing visuals that depict people in active lifestyle scenarios.

Advertisers can also use Imagen 2 to make variations of existing top-performing images, expanding their creative options.

Diversity Drives Results

Pallavi Naresh, a Group Product Manager at Google Ads, states that using a diverse range of creative assets in campaigns can improve performance:

“Great creative drives results — we found that advertisers who improve their Performance Max Ad Strength to ‘Excellent’ see 6% more conversions on average.”

Google is integrating Gemini machine learning models into the platform to support creative diversity in Performance Max campaigns.

This will enhance Performance Max’s ability to automatically generate varied, high-quality text and image ad assets.

Asset Variety Boosts Ad Strength

Google is changing how it calculates Ad Strength scores for Performance Max campaigns by placing greater weight on the quantity and variety of ad assets.

To help advertisers optimize their asset mix, Google suggests the following:

  • Add more assets, including AI-generated adaptations of existing creatives.
  • Leveraging design platforms, like Canva, that allow you to import images directly into campaigns.
  • Incorporating video, as Google says at least one video in a campaign can boost conversions significantly.

To help advertisers utilize more video, Google is expanding auto-generated video creation using Merchant Center product data across eligible campaigns.

Upcoming Feature: Share Ad Previews

In March, Google plans to launch a new feature enabling the sharing of advertising previews through links without requiring a Google Ads login.

This update could streamline the creative development process and improve collaboration between different teams working on ads.

Looking Ahead

For Performance Max campaigns, the key advice based on Google’s announcements is to focus on maximizing both the quantity and quality of assets.

Leveraging the new generative AI capabilities to expand image and text options while carefully reviewing them for relevance is likely the best path to improved performance.

As ad platforms grow increasingly sophisticated, dedicating time and resources to thoughtful, creative development with the help of AI will become steadily more important.

Google Announces Gemma: Laptop-Friendly Open Source AI via @sejournal, @martinibuster

Google released an open source large language model based on the technology used to create Gemini that is powerful yet lightweight, optimized to be used in environments with limited resources like on a laptop or cloud infrastructure.

Gemma can be used to create a chatbot, content generation tool and pretty much anything else that a language model can do. This is the tool that SEOs have been waiting for.

It is released in two versions, one with two billion parameters (2B) and another one with seven billion parameters (7B). The number of parameters indicates the model’s complexity and potential capability. Models with more parameters can achieve a better understanding of language and generate more sophisticated responses, but they also require more resources to train and run.

The purpose of releasing Gemma is to democratize access to state of the art Artificial Intelligence that is trained to be safe and responsible out of the box, with a toolkit to further optimize it for safety.

Gemma By DeepMind

The model is developed to be lightweight and efficient which makes it ideal for getting it into the hands of more end users.

Google’s official announcement noted the following key points:

  • “We’re releasing model weights in two sizes: Gemma 2B and Gemma 7B. Each size is released with pre-trained and instruction-tuned variants.
  • A new Responsible Generative AI Toolkit provides guidance and essential tools for creating safer AI applications with Gemma.
  • We’re providing toolchains for inference and supervised fine-tuning (SFT) across all major frameworks: JAX, PyTorch, and TensorFlow through native Keras 3.0.
  • Ready-to-use Colab and Kaggle notebooks, alongside integration with popular tools such as Hugging Face, MaxText, NVIDIA NeMo and TensorRT-LLM, make it easy to get started with Gemma.
  • Pre-trained and instruction-tuned Gemma models can run on your laptop, workstation, or Google Cloud with easy deployment on Vertex AI and Google Kubernetes Engine (GKE).
  • Optimization across multiple AI hardware platforms ensures industry-leading performance, including NVIDIA GPUs and Google Cloud TPUs.
  • Terms of use permit responsible commercial usage and distribution for all organizations, regardless of size.”

Analysis Of Gemma

According to an analysis by an Awni Hannun, a machine learning research scientist at Apple, Gemma is optimized to be highly efficient in a way that makes it suitable for use in low-resource environments.

Hannun observed that Gemma has a vocabulary of 250,000 (250k) tokens versus 32k for comparable models. The importance of that is that Gemma can recognize and process a wider variety of words, allowing it to handle tasks with complex language. His analysis suggests that this extensive vocabulary enhances the model’s versatility across different types of content. He also believes that it may help with math, code and other modalities.

It was also noted that the “embedding weights” are massive (750 million). The embedding weights are a reference to the parameters that help in mapping words to representations of their meanings and relationships.

An important feature he called out is that the embedding weights, which encode detailed information about word meanings and relationships, are used not just in processing input part but also in generating the model’s output. This sharing improves the efficiency of the model by allowing it to better leverage its understanding of language when producing text.

For end users, this means more accurate, relevant, and contextually appropriate responses (content) from the model, which improves its use in conetent generation as well as for chatbots and translations.

He tweeted:

“The vocab is massive compared to other open source models: 250K vs 32k for Mistral 7B

Maybe helps a lot with math / code / other modalities with a heavy tail of symbols.

Also the embedding weights are big (~750M params), so they get shared with the output head.”

In a follow-up tweet he also noted an optimization in training that translates into potentially more accurate and refined model responses, as it enables the model to learn and adapt more effectively during the training phase.

He tweeted:

“The RMS norm weight has a unit offset.

Instead of “x * weight” they do “x * (1 + weight)”.

I assume this is a training optimization. Usually the weight is initialized to 1 but likely they initialize close to 0. Similar to every other parameter.”

He followed up that there are more optimizations in data and training but that those two factors are what especially stood out.

Designed To Be Safe And Responsible

An important key feature is that it is designed from the ground up to be safe which makes it ideal for deploying for use. Training data was filtered to remove personal and sensitive information. Google also used reinforcement learning from human feedback (RLHF) to train the model for responsible behavior.

It was further debugged with manual re-teaming, automated testing and checked for capabilities for unwanted and dangerous activities.

Google also released a toolkit for helping end-users further improve safety:

“We’re also releasing a new Responsible Generative AI Toolkit together with Gemma to help developers and researchers prioritize building safe and responsible AI applications. The toolkit includes:

  • Safety classification: We provide a novel methodology for building robust safety classifiers with minimal examples.
  • Debugging: A model debugging tool helps you investigate Gemma’s behavior and address potential issues.
  • Guidance: You can access best practices for model builders based on Google’s experience in developing and deploying large language models.”

Read Google’s official announcement:

Gemma: Introducing new state-of-the-art open models

Featured Image by Shutterstock/Photo For Everything

Google Launches Gemini Business & Enterprise For Workspace Users via @sejournal, @MattGSouthern

Google launches AI assistant Gemini for Workspace, available in 2 pricing tiers for business and enterprise.

  • Google rebranded its AI assistant Duet as Gemini and made it more widely available.
  • Gemini allows natural conversations to generate ideas, summaries, and more for Workspace users.
  • Google launched Gemini Business and Gemini Enterprise pricing plans for small teams and heavy AI users.
Google Responds To Evidence Of Reviews Algorithm Bias via @sejournal, @martinibuster

Google responded to a small publisher whose article offered a step by step walkthrough of how big corporate publishers are manipulating the Google Reviews System Algorithm and getting away with it, demonstrating what appears to be a bias towards big brands that negatively impacts small independent publishers.

HouseFresh Google Algorithm Exposé

The story begins with a post titled, How Google is killing independent sites like ours, published on the HouseFresh website. It published what it asserted was evidence that several corporate review sites gamed Google’s algorithm by creating the perception of a hands-on reviews for what HouseFresh maintains were not actual reviews.

For example, it noted how many of the publishers ranked an expensive air purifier that HouseFresh (and Consumer Reports) reviewed and found to perform worse than less expensive alternatives, used more energy and required spending $199.98/year on purifier replacements. Yet the big brand sites gave the product glowing reviews, presumably because the high cost results in higher affiliate earnings.

Remarkably, they showed how the product photos from different big brand publishers were sourced from the same photographer in what appears to be the exact same location, strongly implying that the individual publishers themselves did not each review the product.

HouseFresh offered a detail takedown of what they insist are instances of Google showing preference to fake reviews.

This is a partial list of sites alleged by HouseFresh of successfully ranking low quality reviews:

  • Better Homes & Gardens
  • Real Simple
  • Dotdash Meredith
  • BuzzFeed
  • Reddit with a spam link dropped by a user with a suspended account
  • Popular Science

HouseFresh published a lucid and rational account demonstrating how Google’s Reviews Systems algorithms allegedly give big brands a pass while small independent websites publishing honest reviews steadily lose traffic under each successive wave Google’s new algorithms.

Google Responds

Google’s SearchLiaison offered a response on X (formerly Twitter) that took the accusations seriously.

Notable in the response are the following facts:

Google does not do manual checks on claims made on webpages (except as part of a reconsideration request after a manual action).

Google’s algorithms do not use phrases designed to imply a hands-on review as a ranking signal.

SearchLiaison tweeted:

“Thank you. I appreciated the thoughtfulness of the post, and the concerns and the detail in it.

I’ve passed it along to our Search team along with my thoughts that I’d like to see us do more to ensure we’re showing a better diversity of results that does include both small and large publications.

One note to an otherwise excellent write-up. The article suggests we do some type of “manual check” on claims made by pages. We do not. That reference and link is about manual reviews we do if a page has a manual *spam* action against it, and files a reconsideration request. That’s entirely different from how our automated ranking systems look to reward content.

Somewhat related, just making a claim and talking about a “rigorous testing process” and following an “E-E-A-T checklist” doesn’t guarantee a top ranking or somehow automatically cause a page to do better.

We talk about E-E-A-T because it’s a concept that aligns with how we try to rank good content. But our automated systems don’t look at a page and see a claim like “I tested this!” and think it’s better just because of that. Rather, the things we talk about with E-E-A-T are related to what people find useful in content. Doing things generally for people is what our automated systems seek to reward, using different signals.

More here: developers.google.com/search/docs/fundamentals/creating-helpful-content#eat

Thank you again for the post. I hope we’ll be doing better in the future for these types of issues.”

Does Google Show Preference To Big Brands?

I’ve been working hands-on in SEO for 25 years and there was a time in the early 2000s when Google showed bias towards big corporate brands based on the amount of PageRank the webpage contained. Google subsequently reduced the influence of PageRank scores which in turn reduced the amount of irrelevant big brand sites cluttering the search results pages (SERPs).

That wasn’t an instance of Google preferring big brands as trustworthy. It was an instance of their algorithms not working the way they intended.

It may very well be there are signals in Google’s algorithm that inadvertently favor big brands.

If I were to guess what kinds of signals are responsible I would guess that it would be signals related to user preferences. The recent Google Navboost testimony in the Google antitrust lawsuit made clear that user interactions are an important ranking-related signal.

That’s my speculation of what I think may be happening, that Google’s trust in user signals is having an inadvertent outcome, which is something I’ve been pointing out for years now (read Google’s Froot Loops Algorithm).

Read the discussion on Twitter:

What do BuzzFeed, Rolling Stone, Forbes, PopSci and Real Simple have in common?

Read the HouseFresh Article:

How Google is killing independent sites like ours

Featured Image by Shutterstock/19 STUDIO

FAQ

Does presenting a rigorous testing process in content influence Google’s ranking?

While presenting a rigorous testing process and claims of thoroughness in content is beneficial for user perception, it alone does not influence Google’s rankings. The response from Google clarifies this aspect:

  • The algorithms focus on factors related to content usefulness as perceived by users, beyond just claims of in-depth testing.
  • Claims of a “rigorous testing process” are not ranking signals in and of themselves.
  • Content creators should focus on genuinely serving their audience’s needs and providing value, as this aligns with Google’s ranking principles.

What measures does Google take to check the accuracy of web page claims?

Google does not perform manual checks on the factual accuracy of claims made by web pages. Their algorithms focus on evaluating content quality and relevance through automated ranking systems. Google’s E-E-A-T concept is designed to align with how they rank useful content, but it does not involve any manual review unless there is a specific spam action reconsideration request. This separates factual scrutiny from automated content ranking mechanisms.

5 Questions Answered About The OpenAI Search Engine via @sejournal, @martinibuster

It was reported that OpenAI is working on a search engine that would directly challenge Google. But details missing from the report raise questions about whether OpenAI is creating a standalone search engine or if there’s another reason for the announcement.

OpenAI Web Search Report

The report published on The Information relates that OpenAI is developing a Web Search product that will directly compete with Google. A key detail of the report is that it will be partly powered by Bing, Microsoft’s search engine. Apart from that there are no other details, including whether it will be a standalone search engine or be integrated within ChatGPT.

All reports note that it will be a direct challenge to Google so let’s start there.

1. Is OpenAI Mounting A Challenge To Google?

OpenAI is said to be using Bing search as part of the rumored search engine, a combination of a GPT-4 with Bing Search, plus something in the middle to coordinate between the two .

In that scenario, what OpenAI is not doing is developing its own search indexing technology, it’s using Bing.

What’s left then for OpenAI to do in order to create a search engine is to devise how the search interface interacts with GPT-4 and Bing.

And that’s a problem that Bing has already solved by using what it Microsoft calls an orchestration layer. Bing Chat uses retrieval-augmented generation (RAG) to improve answers by adding web search data to use as context for the answers that GPT-4 creates. For more information on how orchestration and RAG works watch the keynote at Microsoft Build 2023 event by Kevin Scott, Chief Technology Officer at Microsoft, at the 31:45 minute mark here).

If OpenAI is creating a challenge to Google Search, what exactly is left for OpenAI to do that Microsoft isn’t already doing with Bing Chat? Bing is an experienced and mature search technology, an expertise that OpenAI does not have.

Is OpenAI challenging Google? A more plausible answer is that Bing is challenging Google through OpenAI as a proxy.

2. Does OpenAI Have The Momentum To Challenge Google?

ChatGPT is the fastest growing app of all time, currently with about 180 million users, achieving in two months what took years for Facebook and Twitter.

Yet despite that head start Google’s lead is a steep hill for OpenAI to climb.  Consider that Google has approximately 3 to 4 billion users worldwide, absolutely dwarfing OpenAI’s 180 million.

Assuming that all 180 million OpenAI users performed an average of 4 searches per day, the daily number of searches could reach 720 million searches per day.

Statista estimates that there are 6.3 million searches on Google per minute which equals over 9 billion searches per day.

If OpenAI is to compete they’re going to have to offer a useful product with a compelling reason to use it. For example, Google and Apple have a captive audience on mobile device ecosystem that embeds them into the daily lives of their users, both at work and at home. It’s fairly apparent that it’s not enough to create a search engine to compete.

Realistically, how can OpenAI achieve that level of ubiquity and usefulness?

OpenAI is facing an uphill battle against not just Google but Microsoft and Apple, too. If we count Internet of Things apps and appliances then add Amazon to that list of competitors that already have a presence in billions of users daily lives.

OpenAI does not have the momentum to launch a search engine to compete against Google because it doesn’t have the ecosystem to support integration into users lives.

3. OpenAI Lacks Information Retrieval Expertise

Search is formally referred to as Information Retrieval (IR) in research papers and patents. No amount of searching in the Arxiv.org repository of research papers will surface papers authored by OpenAI researchers related to information retrieval. The same can be said for searching for information retrieval (IR) related patents. OpenAI’s list of research papers also lacks IR related studies.

It’s not that OpenAI is being secretive. OpenAI has a long history of publishing research papers about the technologies they’re developing. The research into IR does not exist. So if OpenAI is indeed planning on launching a challenge to Google, where is the smoke from that fire?

It’s a fair guess that search is not something OpenAI is developing right now. There are no signs that it is even flirting with building a search engine, there’s nothing there.

4. Is The OpenAI Search Engine A Microsoft Project?

There is substantial evidence that Microsoft is furiously researching how to use LLMs as a part of a search engine.

All of the following research papers are classified as belonging to the fields of Information Retrieval (aka search), Artificial Intelligence, and Natural Language Computing.

Here are few research papers just from 2024:

Enhancing human annotation: Leveraging large language models and efficient batch processing
This is about using AI for classifying search queries.

Structured Entity Extraction Using Large Language Models
This research paper discovers a way to extracting structured information from unstructured text (like webpages). It’s like turning a webpage (unstructured data) into a machine understandable format (structured data).

Improving Text Embeddings with Large Language Models (PDF version here)
This research paper discusses a way to get high-quality text embeddings that can be used for information retrieval (IR). Text embeddings is a reference to creating a representation of text in a way that can be used by algorithms to understand the semantic meanings and relationships between the words.

The above research paper explains the use:

“Text embeddings are vector representations of natural language that encode its semantic information. They are widely used in various natural language processing (NLP) tasks, such as information retrieval (IR), question answering…etc. In the field of IR, the first-stage retrieval often relies on text embeddings to efficiently recall a small set of candidate documents from a large-scale corpus using approximate nearest neighbor search techniques.”

There’s more research by Microsoft that relates to search, but these are the ones that are specifically related to search together with large language models (like GPT-4.5).

Following the trail of breadcrumbs leads directly to Microsoft as the technology powering any search engine that OpenAI is supposed to be planning… if that rumor is true.

5. Is Rumor Meant To Steal Spotlight From Gemini?

The rumor that OpenAI is launching a competing search engine was published on February 14th. The next day on February 15th Google announced the launch of Gemini 1.5, after announcing Gemini Advanced on February 8th.

Is it a coincidence that OpenAI’s announcement completely overshadowed the Gemini announcement the next day? The timing is incredible.

At this point the OpenAI search engine is just a rumor.

Featured Image by Shutterstock/rafapress

Consent Mode V2: Google Shares Shares Key Details For Advertisers via @sejournal, @MattGSouthern

Google Ads Liaison Ginny Marvin recently highlighted critical updates regarding Google’s enforcement of its EU User Consent Policy.

Google is strengthening enforcement around consent requirements for European Economic Area (EEA) traffic.

As part of this, the company has introduced consent mode version two (v2), which includes parameters for ad personalization and remarketing consent signals.

Advertisers must take action by March 2024 or risk losing critical advertising capabilities in the region.

A Google help page reads:

“To keep using measurement, ad personalisation and remarketing features, you must collect consent for use of personal data from end users based in the EEA and share consent signals with Google. The requirements also apply if you are using Google Analytics data with a Google service.”

Per Marvin’s posts, here’s what advertisers need to know about the pending deadline.

The March Deadline: A Call to Action For Advertisers

Google’s consent mode, which allows advertisers to adjust their Google tag settings based on user consent, is being updated.

The update adds specific parameters to capture consent for ad personalization and remarketing purposes.

The updated consent mode will feature two new parameters: ad_user_data and ad_personalization, which sends consent signals related to personalized advertising.

Advertisers that don’t adopt the new framework by the deadline will lose the ability to serve personalized and remarketed ads.

Transitioning To Google Analytics 4 Recommended

Marvin recommended that Universal Analytics 360 users transition to Google Analytics 4 (GA4) as soon as possible to maintain key advertising capabilities for website traffic from the EEA.

She noted that GA4 now has a new consent setting that allows you to quickly verify that consent signals are being properly transmitted.

Additionally, GA4 offers the enhanced conversions feature, which uses first-party conversion data to provide a more detailed and aggregated view of conversion behaviors.

Marvin pointed out two key benefits of enhanced conversions for advertisers:

“An advantage of implementing enhanced conversions in GA4 rather than only in Google Ads is that user-provided data can be used for additional purposes (such as demographics and interests, and paid & organic measurement).”

Q&A Insights

Marvin concluded her series of posts with a Q&A session addressing common concerns:

  • Consent Mode V2 Deadline: While no specific date is provided, enforcement will begin in March.
  • UK Traffic: UK organizations advertising in the EEA must also implement updates.
  • Conversion Measurement: Without consent mode v2, future remarketing and personalization to audiences will not be possible.

Other Considerations

Here are additional considerations for advertisers, per Marvin’s social media posts:

  • Consider working with a certified consent management platform (CMP) to build and configure a compliant consent banner. Google recommends working with one of its certified CMP partners.
  • Ensure existing remarketing tags/audiences are configured to honor the new consent parameters. Test that your tags behave appropriately based on user consent choices.
  • Review your current ad measurement strategy and ensure you have alternative conversion tracking that doesn’t rely on cookies/advertising IDs in preparation for the post-third-party cookie landscape.
  • Take time now to educate internal stakeholders on impending consent requirements and why they are essential for maintaining access to users in the EEA market.
  • Keep an eye out for any additional updates from Google as you get closer to the enforcement deadline in March.

Doing this now will ensure minimal disruption to your advertising capabilities in the EEA.

FAQ

What is Consent Mode V2, and how does it impact ad personalization and remarketing?

Consent Mode V2 is an updated framework introduced by Google to help advertisers comply with European Economic Area (EEA) consent requirements for online advertising.

This new version features specific parameters for capturing end-user consent related to ad personalization (ad_personalization) and remarketing (ad_user_data).

Advertisers in the EEA, or those targeting EEA traffic, must implement these changes by March 2024 to maintain the ability to deliver personalized and remarketed ads. Failure to adopt Consent Mode V2 could lead to a significant loss of advertising capabilities within the region.

Why is adopting Google Analytics 4 (GA4) recommended for Universal Analytics 360 users?

Universal Analytics 360 users are strongly encouraged to transition to Google Analytics 4 (GA4) because GA4 has a new consent setting that ensures consent signals are correctly shared.

Moreover, GA4 includes the ‘enhanced conversions’ feature, which relies on first-party data to provide an in-depth, aggregated view of conversion activities.

By transitioning to GA4, advertisers maintain key advertising functionalities and gain additional insights into demographics, interests, and paid and organic measurements that are not exclusively dependent on Google Ads.

What actions should advertisers take in light of the strengthened enforcement around EEA traffic consent requirements?

  • Adopt Consent Mode V2 before the March deadline to preserve the ability to serve personalized and remarketed ads to EEA audiences.
  • Engage with a certified consent management platform (CMP) to create a consent banner compliant with policy requirements.
  • Ensure existing remarketing tags and audiences respect the new consent parameters and are tested for proper functionality based on user consent.
  • Develop alternative conversion tracking methods that are not reliant on cookies or advertising IDs in anticipation of the upcoming decline in third-party cookie usage.
  • Update internal stakeholders on the new consent requirements and emphasize their importance for retaining access to the EEA user market.


Featured Image: Mamun sheikh K/Shutterstock